Machine Learning-based Anomaly Detection in IOT Sensing Devices for Optimal Security

The Internet of Things (IoT) is well-known as a new detecting paradigm for interacting with the real world in Industry 4.0. With IoT, major security concern arises in data communication between remote location and the data server. Data obtained from the different sensor devices have to be transmitted securely. Implementing Machine Learning algorithms increases the security as well as the efficiency of the IoT devices. In this research work, MQTT protocol is implemented for data transmission services in Internet of Things enabled devices. The research utilized historical information from a smart manufacturing plant’s tracking sensor, control devices, and IoT cameras. The findings of this research work 5rimproved plant efficacy and security, resulting in quicker and more efficient reactions to uncommon incidents. Outcomes indicate a considerable influence on intelligent manufacturing plant effectiveness and security. Advanced detection of anomalies led to quicker and more efficient reactions to odd events, reducing significant occurrences and enhancing safety. Moreover, technique improvement and IoT infrastructure enhanced productivity by minimizing downtime and maximizing resource use. The suggested study compares machine learning-based protection measures to past studies on IoT protection and identifying anomalies in industrial settings, demonstrating their usefulness. Researchers saw a rise of 14% in the detection of anomalies and a 2% drop in false positives after training machine learning models.